Robustness to affine transformations. To test the robustness to affine transformations of CapsNets+REM, we used expanded MNIST: a dataset composed by padded and translated MNIST, in which each example is an MNIST digit placed randomly on a black background of 40×40 pixels. We used the affNIST dataset as test set, in which each example is an MNIST digit with a random small affine transformation. We tested an under-trained CapsNet with early stopping which achieved 99.22% accuracy on the expanded MNIST test set as in  [1]}, [2]}. We also trained these models until convergence.
We can see in Table REF  that the under-trained networks entropies are high. Instead, a well-trained CapsNet+REM can be robust to affine transformations and have a low entropy.
